Institute for Mathematical Behavioral Sciences
CONFERENCE ON � HUMAN AND MACHINE LEARNING�
March 13-15, 2009
ABSTRACTS and PAPERS
WILLIAM H. BATCHELDER, Cognitive Sciences, UC Irvine
"Learning Theory: History, Formalisms, and Perennial Issues"
LI DENG, Speech Research Group,
�Acoustic Modeling in Automatic Speech Recognition Overview of Current State and Research Challenges �
"Structured Speech Modeling"
JEAN-CLAUDE FALMAGNE, Cognitive Sciences,
�Learning Spaces--Concepts, Results, Applications�
TOM GRIFFITHS, Department of Psychology, UC Berkeley
�Connecting human and machine learning via probabilistic models of cognition �
"Analyzing human feature learning as non-parametric Bayesian influence"
"Markov chain Monte Carlo with people"
"Categorization as nonparametric Bayesian Density Estimation"
TONY JEBARA, Computer Science,
"Learning Networks of Places and People from Location Data�
(Abstract) (Algorithm Paper)
MICHAEL JORDAN, EECS, Statistics, UC Berkeley
�Combinatorial Stochastic Processes and Nonparametric Bayesian Modeling�
"Shared segmentation of natural scenes using dependent Pitman-Yor processes"
"Hierarchal Bayesian nonparametric models with applications"
MICHAEL LITTMAN, Computer Science, Rutgers
�Initial explorations of cognitive reinforcement learning�
DeLIANG WANG, Computer Science and Engineering, Ohio State University
�Cocktail Party Processing�